Machine learning is all about representing data in high dimensional probability models. A key computational bottleneck is the statistical inference, to compute statistics in these models, which is often done by time consuming Monte Carlo sampling. In principle, quantum systems could provide an alternative for these computations. If one can implement a probability distribution in the quantum state, the statistics can be obtained by repeated measurement and thus accellerate the inference computation. This could potentially be realized by a form of Noisy Intermediate-Scale Quantum (NISQ) technology. In this talk we show how the quantum Boltzmann machine (QBM) can represent a classical data distribution as the ground state of a quantum Hamiltonian system. The QBM can learn many more supervised and unsupervised problems than the classical Boltzmann Machine. In addition to computational efficiency, the quantum implementation may also yield novel functionality. The quantum state represents quantum statistics that result from entanglement and signal non-local events that violate the Bell inequality and increase the mutual information between subsystems. At the same time, these statistics are fully consistent with the learned classical data distribution. We propose to investigate how these quantum features can be exploited in machine learning applications.